Browsing by Author "Ghimire, Ram Hari"
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Publication Adenocarcinoma among Patients Diagnosed with Lung Cancer in a Tertiary Care Centre: A Descriptive Cross-sectional Study(Nepal Medical Association, 2022) Ansari, Farmud; Ghimire, Ram Hari; Shrestha, Nischal; Yadav, SushilAbstract Introduction: Lung cancer is the leading cause of cancer deaths all over the world and adenocarcinoma is the most common type. Diagnosis is made usually at an advanced stage of lung cancer in patients, making it nearly impossible to cure. The aim of this study is to find out prevalence of adenocarcinoma among patients diagnosed with lung cancer in a tertiary care centre. Methods: A descriptive cross-sectional study was carried out in the Department of Pulmonary Medicine of a tertiary care centre among 69 patients from October, 2018 to September, 2019. Ethical approval was taken from the Institutional Review Committee of a tertiary care hospital (Reference number: 54/2018). A convenience sampling technique was used. Data were entered and analysed using the Statistical Package for the Social Sciences version 21.0. Point estimate at 90% Confidence Interval and descriptive statistics were interpreted as frequency, percentage, or as mean and standard deviations. Results: Among 69 lung cancer patients, adenocarcinoma was seen in 27 (39.13%) (29.47-48.79 at 90% Confidence Interval). Out of 27, 10 (37.04%) were male and 17 (62.96%) were female. Chronic obstructive pulmonary disease was the major comorbidity seen among 17 (62.96%) patients. Conclusions: The prevalence of adenocarcinoma was similar to other studies done in similar settings.Publication Adenocarcinoma among Patients Diagnosed with Lung Cancer in a Tertiary Care Centre: A Descriptive Cross-sectional Study(Nepal Medical Association, 2022) Ansari, Farmud; Ghimire, Ram Hari; shrestha, Nischal; Yadav, SushilAbstract: Introduction: Lung cancer is the leading cause of cancer deaths all over the world and adenocarcinoma is the most common type. Diagnosis is made usually at an advanced stage of lung cancer in patients, making it nearly impossible to cure. The aim of this study is to find out prevalence of adenocarcinoma among patients diagnosed with lung cancer in a tertiary care centre. Methods: A descriptive cross-sectional study was carried out in the Department of Pulmonary Medicine of a tertiary care centre among 69 patients from October, 2018 to September, 2019. Ethical approval was taken from the Institutional Review Committee of a tertiary care hospital (Reference number: 54/2018). A convenience sampling technique was used. Data were entered and analysed using the Statistical Package for the Social Sciences version 21.0. Point estimate at 90% Confidence Interval and descriptive statistics were interpreted as frequency, percentage, or as mean and standard deviations. Results: Among 69 lung cancer patients, adenocarcinoma was seen in 27 (39.13%) (29.47-48.79 at 90% Confidence Interval). Out of 27, 10 (37.04%) were male and 17 (62.96%) were female. Chronic obstructive pulmonary disease was the major comorbidity seen among 17 (62.96%) patients. Conclusions: The prevalence of adenocarcinoma was similar to other studies done in similar settings.Publication Diagnostic Accuracy of Artificial Intelligence-Based Chest X-Ray reading for screening of Tuberculosis(Nepal Health Research Council, 2024) Shrestha, Ruby Maka; Paudel, Ram Chandra; Adhikari, Puspanjali; Ghimire, Ram Hari; Gurung, Karma; Shrestha, RajeevBackground: Tuberculosis remains a public health challenge in Nepal and ranks as the seventh leading cause of death in the country. The END Tuberculosis strategy stresses - the screening for symptoms alone may not suffice; additional screening tools such as a chest radiograph may facilitate referral for diagnosis of tuberculosis. The study aims to evaluate the diagnostic accuracy of artificial intelligence (AI) based Chest X-ray and compare it with the human reading (radiologist), using GeneXpert-MTB RIF Assay for tuberculosis case detection. Methods: Tuberculosis-suspected patients with a history of cough were screened using chest X-rays at two study sites (Dhulikhel Hospital and Nobel Medical College). The reading of AI qXR software was compared with radiologists reading who were blinded of the results generated by the software. Results: The sensitivity of the test by qXR-based AI reading was 100%, (95% CI: 40 – 100%) and specificity 80% (95% CI: 73 – 87%), whereas the sensitivity of the test by the radiologist was 100%, (95% CI: 40 – 100%); and specificity 62% (95% CI: 53 – 70%). Conclusions: Higher sensitivity and specificity were observed for both qXR-based AI and Radiographer readings for the diagnosis of TB. Keywords: Artificial Intelligence; chest X-ray; diagnostic accuracy; screening; tuberculosis.